An x-ray image besides images of patient organs projections generally involves images of parts of a device (e.g. collimator) and air projections. A region of interest is usually meant as that part of an image where there is the image of patient organs projections only. The necessity to determine the brightness level correctly occurs for example in the following cases:
At digital image visualization on a display of the monitor
For exposure control during the acquisition of series of x-ray images.
The x-ray image visualization with the correct brightness and contrast levels contributes to better understanding the x-ray image and right diagnosing respectively. While acquiring series of sequential images knowing the brightness level corresponding to the region of interest in the previous image the digital detector exposure time can be correctly set to acquire the next image. Correctly chosen exposure allows acquiring x-ray images of considerable higher quality without dark and/or overexposed regions with optimal noise-to-signal ratio in the region of interest. The standard frequency of the x-ray image series is 30 frames per second, therefore it is extremely important to determine the brightness level fast enough to be able to adjust the exposure time and/or x-ray tube characteristics. It is also necessary that the brightness level calculation method be stable in course of calculations performed on series of sequential images.
The method [R. Gonzalez, R. Woods, S. Eddins. Digital Image Processing Using MATLAB (DIPUM). Technosphera, 2006, p. 32] for image brightness level determination is known. According to that method the brightness level is calculated as a mean value between the minimum and maximum brightness valuesLevel=(Valueα+Value1-α)/2
Valueα is the α-quantile of brightness of pixels over the image. Parameter α is to be chosen sufficiently small, not more than 0.01. This method does not provide necessary calculation accuracy of the brightness level in case of presence of air and/or collimator regions within the image.
The closest technical solution chosen as a prototype is the method for determination of the brightness level described in [Patent EP 0 409 206 B1, p. 6, published 01.10.1997,]. In accordance with the prototype the method comprises of reading out the digital image data into the main memory of the device and performing after that the following calculations:
The image histogram with the bin width equal to 1 is calculated.
The level A of brightness at which pixels of lower brightness considered the background once is calculated.
The histogram within the interval where pixel brightness is more than A is analyzed. The brightness MVP corresponding to the maximum histogram frequency in the said interval is calculated.
Initial values for image visualization is chosen: window level WL0=MVP and the window width WW0=2×(MVP−A).
The parameter ΔWW=WW0/2 is calculated.
Using a neural net the quality index {Qi}i=08 is calculated for each pair of values (WL0±ΔWW,WW0±ΔWW).
Using the hill climbing method, such a pair of values (WLc,WWc) at which the quality index Qc has its maximum value is calculated. During an iterative procedure the parameter ΔWW is corrected.
The quality index is evaluated by means of a feedforward neural network, (hereinafter—neural network), having one hidden layer and one neural in the output layer with the sigmoid activation functions of neurons. The window level and window width (WLc,WWc), correlating to the maximum value of the quality index Qc, is considered optimal parameters for image visualization.
One or several images for which a skilled operator sets desirable values of the window level and window width (WLG,WWG) are used for training. Then a table consisting out of 25 values is made.(WLG±ΔWWG/2±ΔWWG/4,WWG±ΔWWG/2±ΔWWG/4)Qi, ΔWWG=WWG/2;
Qi—predetermined values of the quality index.
Input arguments of neural network (five or even more) are calculated for each pair) (WLi,WWi). The quality index Qi, correlating to the appropriate pair (WLi,WWi), is used as a target value. So, marking desirable parameters of the window level and window width on the given image set an operator gets data for neural network training and after that trains it.
Disadvantages of the method according to the prototype are as follows:
Being applied to exposure control task when brightness level is only to be determined the method provides redundant information.
By means of the method the algorithm stability in course of calculation of series of imagers is not controlled. It is important for exposure control during the acquisition of series of imagers.